





from __future__ import annotations

class   A							:



      def __init__(						self:    Dict		,	_lowerCAmelCase:    int							)    ->     None:
         '''simple docstring'''

         UpperCAmelCase_           =order

         # a_{0} ... a_{k}
         UpperCAmelCase_           =[1.0] + [0.0] * order
         # b_{0} ... b_{k}
         UpperCAmelCase_           =[1.0] + [0.0] * order

         # x[n-1] ... x[n-k]
         UpperCAmelCase_           =[0.0] * self.order
         # y[n-1] ... y[n-k]
         UpperCAmelCase_           =[0.0] * self.order



      def 						lowerCAmelCase__     (						self:    Optional[Any]		,	_lowerCAmelCase:    list[float]		,	_lowerCAmelCase:    list[float]							)    ->     None:
         '''simple docstring'''

         if len(_lowerCAmelCase							) < self.order:
            UpperCAmelCase_           =[1.0, *a_coeffs]

         if len(_lowerCAmelCase							) != self.order + 1:
            UpperCAmelCase_           =(
                F'Expected a_coeffs to have {self.order + 1} elements '
                F'for {self.order}-order filter, got {len(_lowerCAmelCase							)}'
            )
            raise ValueError(_lowerCAmelCase							)

         if len(_lowerCAmelCase							) != self.order + 1:
            UpperCAmelCase_           =(
                F'Expected b_coeffs to have {self.order + 1} elements '
                F'for {self.order}-order filter, got {len(_lowerCAmelCase							)}'
            )
            raise ValueError(_lowerCAmelCase							)

         UpperCAmelCase_           =a_coeffs
         UpperCAmelCase_           =b_coeffs






      def 						lowerCAmelCase__     (						self:    Dict		,	_lowerCAmelCase:    float							)    ->     float:
         '''simple docstring'''

         UpperCAmelCase_           =0.0

         # Start at index 1 and do index 0 at the end.
         for i in range(1		,	self.order + 1							):
            result += (
                self.b_coeffs[i] * self.input_history[i - 1]
                - self.a_coeffs[i] * self.output_history[i - 1]
            )

         UpperCAmelCase_           =(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

         UpperCAmelCase_           =self.input_history[:-1]
         UpperCAmelCase_           =self.output_history[:-1]

         UpperCAmelCase_           =sample
         UpperCAmelCase_           =result

         return result






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase    :   int						=			{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			['ConditionalDetrFeatureExtractor']
		lowercase    :   int						=			['ConditionalDetrImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   Optional[int]						=			[
		    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ConditionalDetrForObjectDetection',
		    'ConditionalDetrForSegmentation',
		    'ConditionalDetrModel',
		    'ConditionalDetrPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

		lowercase    :   Optional[int]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)